Univariate - MA Data Analysis
Akihiko Mori
#‘Metabolism of A Noodle Restaurant about Food Loss and Food Waste: Micro-Level Material Flow Model and Tobit Regression Analysis’
Library
Import Data from file
Check imported data
head(df)## date day is_closed food_loss_kg food_waste_kg solid_waste_kg
## 1 2022-09-16 Fri FALSE 9.50 6.55 2.50
## 2 2022-09-17 Sat FALSE 12.25 2.80 0.60
## 3 2022-09-18 Sun FALSE 6.50 3.25 0.85
## 4 2022-09-20 Tue FALSE 13.10 0.70 0.30
## 5 2022-09-21 Wed FALSE 5.70 1.10 0.45
## 6 2022-09-22 Thu FALSE 7.25 0.80 0.35
## liquid_waste_kg customers fulls halfs takeouts liquors sales container
## 1 4.05 42 36 4 15 2 1080.48 0
## 2 2.20 42 30 6 12 2 861.76 0
## 3 2.40 27 24 2 10 1 629.49 0
## 4 0.40 13 10 2 12 4 635.33 0
## 5 0.65 15 10 3 10 1 533.32 0
## 6 0.45 14 10 2 16 1 680.46 0
## temp_c humi_p prcp_mm TS_noodle_kg TS_water_kg TS_bones_kg TS_veg_kg
## 1 9.04 89.5 4.1 -7.950 -34.450 -8.7450 -4.982
## 2 7.00 92.5 1.0 -6.750 -29.250 -7.4250 -4.230
## 3 9.61 81.1 0.0 -5.250 -22.750 -5.7750 -3.290
## 4 5.66 74.1 0.0 -3.450 -14.950 -3.7950 -2.162
## 5 7.35 76.7 0.0 -3.225 -13.975 -3.5475 -2.021
## 6 10.78 66.7 0.0 -4.050 -17.550 -4.4550 -2.538
## TS_meat_kg TS_condi_kg TS_Broth_kg TS_Stock_kg TS_FL_kg TS_FL_bone_kg
## 1 -2.12 -0.7950 1.029300e-15 29.68 11.342 -8.7450
## 2 -1.80 -0.6750 -1.110223e-15 25.20 9.630 -7.4250
## 3 -1.40 -0.5250 1.110223e-15 19.60 7.490 -5.7750
## 4 -0.92 -0.3450 -7.569254e-16 12.88 4.922 -3.7950
## 5 -0.86 -0.3225 -1.514838e-16 12.04 4.601 -3.5475
## 6 -1.08 -0.4050 3.128362e-16 15.12 5.778 -4.4550
## TS_FL_veg_kg TS_FL_meat_kg TS_FP_kg FL_noodle_kg FL_water_kg FL_bones_kg
## 1 -2.332 -0.2650 47.70 -6.658879 -28.85514 -7.324766
## 2 -1.980 -0.2250 40.50 -8.586449 -37.20794 -9.445093
## 3 -1.540 -0.1750 31.50 -4.556075 -19.74299 -5.011682
## 4 -1.012 -0.1150 20.70 -9.182243 -39.78972 -10.100467
## 5 -0.946 -0.1075 19.35 -3.995327 -17.31308 -4.394860
## 6 -1.188 -0.1350 24.30 -5.081776 -22.02103 -5.589953
## FL_veg_kg FL_meat_kg FL_condi_kg FL_Broth_kg FL_Stock_kg FL_FL_kg
## 1 -4.172897 -1.775701 -0.6658879 -1.332268e-15 24.85981 9.50
## 2 -5.380841 -2.289720 -0.8586449 1.554312e-15 32.05607 12.25
## 3 -2.855140 -1.214953 -0.4556075 1.332268e-15 17.00935 6.50
## 4 -5.754206 -2.448598 -0.9182243 1.373771e-15 34.28037 13.10
## 5 -2.503738 -1.065421 -0.3995327 8.674266e-16 14.91589 5.70
## 6 -3.184579 -1.355140 -0.5081776 -1.110223e-15 18.97196 7.25
## FL_FL_bone_kg FL_FL_veg_kg FL_FL_meat_kg FL_FP_kg Broth_diff Final_Prod_diff
## 1 -7.324766 -1.953271 -0.2219626 39.95327 -4.820187 -7.746729
## 2 -9.445093 -2.518692 -0.2862150 51.51869 6.856075 11.018692
## 3 -5.011682 -1.336449 -0.1518692 27.33645 -2.590654 -4.163551
## 4 -10.100467 -2.693458 -0.3060748 55.09346 21.400374 34.393458
## 5 -4.394860 -1.171963 -0.1331776 23.97196 2.875888 4.621963
## 6 -5.589953 -1.490654 -0.1693925 30.49065 3.851963 6.190654
## daily_total_served tueD wedD thuD friD satD tueE wedE thuE friE satE
## 1 47.70 0 0 0 1 0 0 0 0 1 0
## 2 40.50 0 0 0 0 1 0 0 0 0 1
## 3 31.50 0 0 0 0 0 -1 -1 -1 -1 -1
## 4 20.70 1 0 0 0 0 1 0 0 0 0
## 5 19.35 0 1 0 0 0 0 1 0 0 0
## 6 24.30 0 0 1 0 0 0 0 1 0 0str(df)## 'data.frame': 169 obs. of 56 variables:
## $ date : chr "2022-09-16" "2022-09-17" "2022-09-18" "2022-09-20" ...
## $ day : chr "Fri" "Sat" "Sun" "Tue" ...
## $ is_closed : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ food_loss_kg : num 9.5 12.2 6.5 13.1 5.7 ...
## $ food_waste_kg : num 6.55 2.8 3.25 0.7 1.1 0.8 1.5 2.65 2.55 2.2 ...
## $ solid_waste_kg : num 2.5 0.6 0.85 0.3 0.45 0.35 0.65 0.7 0.8 0.8 ...
## $ liquid_waste_kg : num 4.05 2.2 2.4 0.4 0.65 0.45 0.85 1.95 1.75 1.4 ...
## $ customers : int 42 42 27 13 15 14 12 35 24 26 ...
## $ fulls : int 36 30 24 10 10 10 11 35 18 25 ...
## $ halfs : int 4 6 2 2 3 2 2 2 3 3 ...
## $ takeouts : int 15 12 10 12 10 16 28 23 25 13 ...
## $ liquors : int 2 2 1 4 1 1 2 3 6 3 ...
## $ sales : num 1080 862 629 635 533 ...
## $ container : int 0 0 0 0 0 0 0 0 0 0 ...
## $ temp_c : num 9.04 7 9.61 5.66 7.35 ...
## $ humi_p : num 89.5 92.5 81.1 74.1 76.7 66.7 75.6 71.3 70.1 74.7 ...
## $ prcp_mm : num 4.1 1 0 0 0 0 0 0 0 0 ...
## $ TS_noodle_kg : num -7.95 -6.75 -5.25 -3.45 -3.23 ...
## $ TS_water_kg : num -34.5 -29.2 -22.8 -14.9 -14 ...
## $ TS_bones_kg : num -8.74 -7.42 -5.78 -3.79 -3.55 ...
## $ TS_veg_kg : num -4.98 -4.23 -3.29 -2.16 -2.02 ...
## $ TS_meat_kg : num -2.12 -1.8 -1.4 -0.92 -0.86 -1.08 -1.6 -2.36 -1.78 -1.58 ...
## $ TS_condi_kg : num -0.795 -0.675 -0.525 -0.345 -0.323 ...
## $ TS_Broth_kg : num 1.03e-15 -1.11e-15 1.11e-15 -7.57e-16 -1.51e-16 ...
## $ TS_Stock_kg : num 29.7 25.2 19.6 12.9 12 ...
## $ TS_FL_kg : num 11.34 9.63 7.49 4.92 4.6 ...
## $ TS_FL_bone_kg : num -8.74 -7.42 -5.78 -3.79 -3.55 ...
## $ TS_FL_veg_kg : num -2.332 -1.98 -1.54 -1.012 -0.946 ...
## $ TS_FL_meat_kg : num -0.265 -0.225 -0.175 -0.115 -0.107 ...
## $ TS_FP_kg : num 47.7 40.5 31.5 20.7 19.4 ...
## $ FL_noodle_kg : num -6.66 -8.59 -4.56 -9.18 -4 ...
## $ FL_water_kg : num -28.9 -37.2 -19.7 -39.8 -17.3 ...
## $ FL_bones_kg : num -7.32 -9.45 -5.01 -10.1 -4.39 ...
## $ FL_veg_kg : num -4.17 -5.38 -2.86 -5.75 -2.5 ...
## $ FL_meat_kg : num -1.78 -2.29 -1.21 -2.45 -1.07 ...
## $ FL_condi_kg : num -0.666 -0.859 -0.456 -0.918 -0.4 ...
## $ FL_Broth_kg : num -1.33e-15 1.55e-15 1.33e-15 1.37e-15 8.67e-16 ...
## $ FL_Stock_kg : num 24.9 32.1 17 34.3 14.9 ...
## $ FL_FL_kg : num 9.5 12.2 6.5 13.1 5.7 ...
## $ FL_FL_bone_kg : num -7.32 -9.45 -5.01 -10.1 -4.39 ...
## $ FL_FL_veg_kg : num -1.95 -2.52 -1.34 -2.69 -1.17 ...
## $ FL_FL_meat_kg : num -0.222 -0.286 -0.152 -0.306 -0.133 ...
## $ FL_FP_kg : num 40 51.5 27.3 55.1 24 ...
## $ Broth_diff : num -4.82 6.86 -2.59 21.4 2.88 ...
## $ Final_Prod_diff : num -7.75 11.02 -4.16 34.39 4.62 ...
## $ daily_total_served: num 47.7 40.5 31.5 20.7 19.4 ...
## $ tueD : int 0 0 0 1 0 0 0 0 0 1 ...
## $ wedD : int 0 0 0 0 1 0 0 0 0 0 ...
## $ thuD : int 0 0 0 0 0 1 0 0 0 0 ...
## $ friD : int 1 0 0 0 0 0 1 0 0 0 ...
## $ satD : int 0 1 0 0 0 0 0 1 0 0 ...
## $ tueE : int 0 0 -1 1 0 0 0 0 -1 1 ...
## $ wedE : int 0 0 -1 0 1 0 0 0 -1 0 ...
## $ thuE : int 0 0 -1 0 0 1 0 0 -1 0 ...
## $ friE : int 1 0 -1 0 0 0 1 0 -1 0 ...
## $ satE : int 0 1 -1 0 0 0 0 1 -1 0 ...names(df)## [1] "date" "day" "is_closed"
## [4] "food_loss_kg" "food_waste_kg" "solid_waste_kg"
## [7] "liquid_waste_kg" "customers" "fulls"
## [10] "halfs" "takeouts" "liquors"
## [13] "sales" "container" "temp_c"
## [16] "humi_p" "prcp_mm" "TS_noodle_kg"
## [19] "TS_water_kg" "TS_bones_kg" "TS_veg_kg"
## [22] "TS_meat_kg" "TS_condi_kg" "TS_Broth_kg"
## [25] "TS_Stock_kg" "TS_FL_kg" "TS_FL_bone_kg"
## [28] "TS_FL_veg_kg" "TS_FL_meat_kg" "TS_FP_kg"
## [31] "FL_noodle_kg" "FL_water_kg" "FL_bones_kg"
## [34] "FL_veg_kg" "FL_meat_kg" "FL_condi_kg"
## [37] "FL_Broth_kg" "FL_Stock_kg" "FL_FL_kg"
## [40] "FL_FL_bone_kg" "FL_FL_veg_kg" "FL_FL_meat_kg"
## [43] "FL_FP_kg" "Broth_diff" "Final_Prod_diff"
## [46] "daily_total_served" "tueD" "wedD"
## [49] "thuD" "friD" "satD"
## [52] "tueE" "wedE" "thuE"
## [55] "friE" "satE"Univariable
Open days
# sample size: open and close days ---------------------------------------
data.frame(obs_days = nrow(df),
open_days = sum(df$is_closed),
closed_days = sum(!df$is_closed))## obs_days open_days closed_days
## 1 169 8 161df %>%
freq_table(is_closed)## # A tibble: 2 × 3
## is_closed n prop
## <lgl> <int> <dbl>
## 1 FALSE 161 95.3
## 2 TRUE 8 4.7Basic Summary of Dependent Variables
# basic summary: dependents ----------------------------------------------------
data.frame(food_loss_waste = c(summary(df$food_loss_kg + df$food_waste_kg)),
food_loss = c(summary(df$food_loss_kg)),
food_waste_all = c(summary(df$food_waste_kg)),
food_waste_liquid = c(summary(df$liquid_waste_kg)),
food_waste_solid = c(summary(df$solid_waste_kg)))## food_loss_waste food_loss food_waste_all food_waste_liquid
## Min. 0.000000 0.000000 0.000000 0.000000
## 1st Qu. 8.250000 6.600000 0.950000 0.550000
## Median 9.500000 7.300000 1.950000 1.400000
## Mean 9.543491 7.460355 2.083136 1.408876
## 3rd Qu. 11.050000 8.150000 2.900000 2.000000
## Max. 17.900000 13.800000 6.550000 4.500000
## food_waste_solid
## Min. 0.0000000
## 1st Qu. 0.3500000
## Median 0.6000000
## Mean 0.6742604
## 3rd Qu. 0.9000000
## Max. 2.9500000df %>%
select(c(food_loss_kg,food_waste_kg,liquid_waste_kg,solid_waste_kg)) %>%
get_summary_stats()## # A tibble: 4 × 13
## variable n min max median q1 q3 iqr mad mean sd se
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 food_loss_… 169 0 13.8 7.3 6.6 8.15 1.55 1.19 7.46 2.69 0.207
## 2 food_waste… 169 0 6.55 1.95 0.95 2.9 1.95 1.48 2.08 1.45 0.111
## 3 liquid_was… 169 0 4.5 1.4 0.55 2 1.45 1.04 1.41 1.02 0.079
## 4 solid_wast… 169 0 2.95 0.6 0.35 0.9 0.55 0.445 0.674 0.51 0.039
## # ℹ 1 more variable: ci <dbl># basic summary: dependents excluding closed days ------------------------------
data.frame(food_loss_waste = c(summary(df$food_loss_kg[!df$is_closed]
+ df$food_waste_kg[!df$is_closed])),
food_loss = c(summary(df$food_loss_kg[!df$is_closed])),
food_waste_all = c(summary(df$food_waste_kg[!df$is_closed])),
food_waste_liquid = c(summary(df$liquid_waste_kg[!df$is_closed])),
food_waste_solid = c(summary(df$solid_waste_kg[!df$is_closed])))## food_loss_waste food_loss food_waste_all food_waste_liquid
## Min. 0.0000 0.000000 0.000000 0.000000
## 1st Qu. 8.4000 6.700000 1.100000 0.650000
## Median 9.6500 7.350000 2.100000 1.500000
## Mean 10.0177 7.831056 2.186646 1.478882
## 3rd Qu. 11.1500 8.400000 2.950000 2.050000
## Max. 17.9000 13.800000 6.550000 4.500000
## food_waste_solid
## Min. 0.000000
## 1st Qu. 0.350000
## Median 0.650000
## Mean 0.707764
## 3rd Qu. 0.950000
## Max. 2.950000df %>%
filter(is_closed == FALSE) %>%
select(c(food_loss_kg,food_waste_kg,liquid_waste_kg,solid_waste_kg)) %>%
get_summary_stats()## # A tibble: 4 × 13
## variable n min max median q1 q3 iqr mad mean sd se
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 food_loss_… 161 0 13.8 7.35 6.7 8.4 1.7 1.11 7.83 2.17 0.171
## 2 food_waste… 161 0 6.55 2.1 1.1 2.95 1.85 1.33 2.19 1.40 0.111
## 3 liquid_was… 161 0 4.5 1.5 0.65 2.05 1.4 1.04 1.48 0.995 0.078
## 4 solid_wast… 161 0 2.95 0.65 0.35 0.95 0.6 0.445 0.708 0.499 0.039
## # ℹ 1 more variable: ci <dbl># summary of--------------------
# 1. number of observations
# 2. Averages
# 3. standard deviations
# 4. Min values
# 4. Max values
stargazer(subset(df[4:7], df$is_closed == FALSE), flip=TRUE,
type = "text",digits=2, out="deps1.txt")##
## ===================================================================
## Statistic food_loss_kg food_waste_kg solid_waste_kg liquid_waste_kg
## -------------------------------------------------------------------
## N 161 161 161 161
## Mean 7.83 2.19 0.71 1.48
## St. Dev. 2.17 1.40 0.50 1.00
## Min 0.00 0.00 0.00 0.00
## Max 13.80 6.55 2.95 4.50
## -------------------------------------------------------------------# Excluding the restaurant closed ---------------
stargazer(subset(df[4:7], df$is_closed == FALSE), flip=TRUE,
type = "text",digits=2, out="deps2.txt")##
## ===================================================================
## Statistic food_loss_kg food_waste_kg solid_waste_kg liquid_waste_kg
## -------------------------------------------------------------------
## N 161 161 161 161
## Mean 7.83 2.19 0.71 1.48
## St. Dev. 2.17 1.40 0.50 1.00
## Min 0.00 0.00 0.00 0.00
## Max 13.80 6.55 2.95 4.50
## -------------------------------------------------------------------Histograms
# bw <- 2 * IQR(df$food_loss_kg) / length(df$food_loss_kg)^(1/3)
# binwidth = bw
# Histogram on food loss + food waste ------------------------------------------
hist_loss_waste <-
ggplot(data = subset(df, is_closed %in% FALSE),
aes(x = food_loss_kg + food_waste_kg)) +
geom_histogram(aes(y = after_stat(density)), bins = 30, colour = 1, fill = "white") +
geom_density(linewidth = 1.5, colour = 4, fill = 4, alpha = 0.15) +
labs(title = "Food Loss and Food Waste - Histogram")
hist_loss_waste
# Histogram on food loss----------------------------------------------------
hist_loss <-
ggplot(data = subset(df, is_closed %in% FALSE), aes(x = food_loss_kg)) +
geom_histogram(aes(y = after_stat(density)), bins = 30, colour = 1, fill = "white") +
geom_density(linewidth = 1.5, colour = 4, fill = 4, alpha = 0.15) +
labs(title = "Food Loss - Histogram")
hist_loss
# Histogram of food waste ----------------------------------------------------
hist_food_waste <-
ggplot(data = subset(df, is_closed %in% FALSE), aes(x = food_waste_kg)) +
geom_histogram(aes(y = after_stat(density)), bins = 30,colour = 1, fill = "white") +
geom_density(linewidth = 1.5, colour = 4, fill = 4, alpha = 0.15) +
labs(title = "Food Waste - Histogram")
hist_food_waste
# Histogram of solid waste ----------------------------------------------------
hist_solid_waste <-
ggplot(data = subset(df, is_closed %in% FALSE), aes(x = solid_waste_kg)) +
geom_histogram(aes(y = after_stat(density)), bins = 30,colour = 1, fill = "white") +
geom_density(linewidth = 1.5, colour = 4, fill = 4, alpha = 0.15) +
labs(title = "Solid Food Waste - Histogram")
hist_solid_waste
# Histogram of liquid waste ----------------------------------------------------
hist_liquid_waste <-
ggplot(data = subset(df, is_closed %in% FALSE), aes(x = liquid_waste_kg)) +
geom_histogram(aes(y = after_stat(density)), bins = 30,colour = 1, fill = "white") +
geom_density(linewidth = 1.5, colour = 4, fill = 4, alpha = 0.15) +
labs(title = "Liquid Food Waste - Histogram")
hist_liquid_waste
grid.arrange(hist_loss_waste,hist_loss,
hist_food_waste,hist_solid_waste,hist_liquid_waste)
Time Series Plots
# Daily Plot on food loss + food waste ---------------------------------
daily_loss_waste <-
ggplot(data = df, aes(x = as.Date(date), y = food_loss_kg + food_waste_kg)) +
geom_line(aes(group = 1), color="dark blue") +
geom_point(aes(shape = is_closed)) +
scale_shape_manual(values=c(16, 4)) +
scale_x_date(date_labels = "%b %d") +
theme(legend.position = c(0.05,0.15)) +
xlab("Date") + ylab("Daily Food Loss and Waste (kg)") +
ggtitle("Daily Food Loss and Waste Trend")
daily_loss_waste
# Daily Plot on food loss ------------------------------------------------
daily_loss <-
ggplot(data = df, aes(x = as.Date(date), y = food_loss_kg)) +
geom_line(color="blue") +
geom_point(aes(shape = is_closed)) +
scale_x_date(date_labels = "%b %d") +
scale_shape_manual(values=c(16, 4))+
theme(legend.position = c(0.9,0.85)) +
xlab("Date") + ylab("Daily Food Loss (kg)") +
ggtitle("Daily Food Loss Trend")
daily_loss
# Daily Plot on food waste -----------------------------------------------
daily_waste <-
ggplot(data = df, aes(x = as.Date(date), y = food_waste_kg)) +
geom_line(color="black") +
geom_point(aes(shape = is_closed)) +
scale_x_date(date_labels = "%b %d") +
scale_shape_manual(values=c(16, 4))+
theme(legend.position = c(0.8,0.85)) +
xlab("Date") + ylab("Daily Food Waste (kg)") +
ggtitle("Daily Food Waste Trend")
daily_waste
# Daily Plot on solid food waste -----------------------------------------
daily_solid_waste <-
ggplot(data = df, aes(x = as.Date(date), y = solid_waste_kg)) +
geom_line(color="dark orange") +
geom_point(aes(shape = is_closed)) +
scale_x_date(date_labels = "%b %d") +
scale_shape_manual(values=c(16, 4))+
theme(legend.position = c(0.8,0.85)) +
xlab("Date") + ylab("Daily Solid Food Waste (kg)") +
ggtitle("Daily Solid Food Waste Trend")
daily_solid_waste
# Daily Plot on liquid food waste ----------------------------------------
daily_liquid_waste <-
ggplot(data = df, aes(x = as.Date(date), y = liquid_waste_kg)) +
geom_line(color="dark blue") +
geom_line(color="blue", linetype = "dashed") +
geom_point(aes(shape = is_closed)) +
scale_x_date(date_labels = "%b %d") +
scale_shape_manual(values=c(16, 4))+
theme(legend.position = c(0.8,0.85)) +
xlab("Date") + ylab("Daily Liquid ood Waste (kg)") +
ggtitle("Daily Liquid Food Waste Trend")
daily_liquid_waste
grid.arrange(daily_loss_waste,daily_loss, daily_waste,
daily_solid_waste,daily_liquid_waste)
# Monthly Plot on food loss and food waste ---------------------------------
monthly_loss_waste <-
ggplot(data = df, aes(x = day_name,
y = food_loss_kg + food_waste_kg, group=1)) +
geom_line(color="dark blue") +
geom_point(aes(shape = is_closed)) +
scale_shape_manual(values=c(16, 4)) +
theme(legend.position = "none") +
# geom_rect(data = df, aes(xmin = date, xmax = dplyr::lead(date),
# ymin = -Inf, ymax = Inf,
# fill = factor(!is_closed)), alpha = .3) +
facet_grid(month_name~.) +
xlab("Date") + ylab("Monthly Food Loss and Waste (kg)") +
ggtitle("Monthly Food Loss and Waste Trend")
monthly_loss_waste
# Monthly Plot on food loss ------------------------------------------------
monthly_loss <-
ggplot(data = df, aes(x = day_name, y = food_loss_kg, group=1)) +
geom_line(color="black") +
geom_point(aes(shape = is_closed)) +
facet_grid(month_name~.) +
scale_shape_manual(values=c(16, 4))+
theme(legend.position = "none") +
xlab("Date") + ylab("Monthly Food Loss (kg)") +
ggtitle("Monthly Food Loss Trend")
monthly_loss
# Monthly Plot on food waste -----------------------------------------------
monthly_waste <-
ggplot(data = df, aes(x = day_name, y = food_waste_kg, group=1)) +
geom_line(color="black") +
geom_point(aes(shape = is_closed)) +
facet_grid(month_name~.) +
scale_shape_manual(values=c(16, 4))+
theme(legend.position = "none") +
xlab("Date") + ylab("Monthly Food Waste (kg)") +
ggtitle("Monthly Food Waste Trend")
monthly_waste
# Monthly Plot on solid food waste -----------------------------------------
monthly_solid_waste <-
ggplot(data = df, aes(x = day_name, y = solid_waste_kg, group=1)) +
geom_line(color="dark orange") +
geom_point(aes(shape = is_closed)) +
facet_grid(month_name~.) +
scale_shape_manual(values=c(16, 4))+
theme(legend.position = "none") +
xlab("Date") + ylab("Monthly Solid Food Waste (kg)") +
ggtitle("Monthly Solid Food Waste Trend")
monthly_solid_waste
# Monthly Plot on liquid food waste ----------------------------------------
monthly_liquid_waste <-
ggplot(data = df, aes(x = day_name, y = liquid_waste_kg, group=1)) +
geom_line(color="blue") +
geom_point(aes(shape = is_closed)) +
facet_grid(month_name~.) +
scale_shape_manual(values=c(16, 4))+
theme(legend.position = "none") +
xlab("Date") + ylab("Monthly Liquid ood Waste (kg)") +
ggtitle("Monthly Liquid Food Waste Trend")
monthly_liquid_waste
# grid.arrange(monthly_loss_waste,monthly_loss, monthly_waste,
# monthly_solid_waste,monthly_liquid_waste)Boxplots
# weekly boxplot on food loss + food waste ----------------------------
boxplot_week_loss_waste <-
ggplot(data = subset(df, is_closed %in% FALSE),
aes(x=day, y=food_loss_kg + food_waste_kg)) +
geom_boxplot(outlier.shape=8, outlier.size=4) +
stat_summary(fun=mean, geom="point", shape=16, size=3) +
labs(title = "Boxplot of Food Loss and Food Waste in Day of the Week",
x = "Week of Day", y = "Food Loss and Food Waste in kg")
boxplot_week_loss_waste
# weekly boxplot on food loss ----------------------------------
boxplot_week_food_loss <-
ggplot(data = subset(df, is_closed %in% FALSE),
aes(x=day, y=food_loss_kg)) +
geom_boxplot(outlier.shape=8, outlier.size=4) +
stat_summary(fun=mean, geom="point", shape=16, size=3) +
labs(title = "Boxplot of Food Loss in Day of the Week",
x = "Week of Day", y = "Food Loss in kg")
boxplot_week_food_loss
# weekly boxplot on food waste ------------------------------------
boxplot_week_food_waste <-
ggplot(data = subset(df, is_closed %in% FALSE),
aes(x=day, y=food_waste_kg)) +
geom_boxplot(outlier.shape=8, outlier.size=4) +
stat_summary(fun=mean, geom="point", shape=16, size=3) +
labs(title = "Boxplot of All Food Waste in Day of the Week",
x = "Week of Day", y = "Food Waste in kg")
boxplot_week_food_waste
# weekly boxplot on solid food waste ------------------------------------
boxplot_week_solidWaste <-
ggplot(data = subset(df, is_closed %in% FALSE),
aes(x=day, y=solid_waste_kg)) +
geom_boxplot(outlier.shape=8, outlier.size=4) +
stat_summary(fun=mean, geom="point", shape=16, size=3) +
labs(title = "Boxplot of Solid Food Waste in Day of the Week",
x = "Week of Day", y = "Solid Food Waste in kg")
boxplot_week_solidWaste
# weekly boxplot on liquid food waste ------------------------------------
boxplot_week_liquidWaste <-
ggplot(data = subset(df, is_closed %in% FALSE),
aes(x=day, y=liquid_waste_kg)) +
geom_boxplot(outlier.shape=8, outlier.size=4) +
stat_summary(fun=mean, geom="point", shape=16, size=3) +
labs(title = "Boxplot of Liquid Food Waste in Day of the Week",
x = "Week of Day", y = "Liquid Food Waste in kg")
boxplot_week_liquidWaste
grid.arrange(boxplot_week_food_loss,boxplot_week_food_waste,
boxplot_week_solidWaste,boxplot_week_liquidWaste)
# monthly boxplot on food loss + food waste ------------------------------------
boxplot_month_loss_waste <-
ggplot(data = subset(df, is_closed %in% FALSE),
aes(x=month_name, y=food_loss_kg+food_waste_kg)) +
geom_boxplot(outlier.shape=8, outlier.size=2) +
stat_summary(fun=mean, geom="point", shape=16, size=2) +
labs(title = "Boxplot of Food Loss and Food Waste in Month",
x = "Month", y = "Food Loss and Waste in kg")
boxplot_month_loss_waste
# monthly boxplot on food loss ------------------------------------
boxplot_month_loss <-
ggplot(data = subset(df, is_closed %in% FALSE),
aes(x=month_name, y=food_loss_kg)) +
geom_boxplot(outlier.shape=8, outlier.size=2) +
stat_summary(fun=mean, geom="point", shape=16, size=2) +
labs(title = "Boxplot of Food Loss in Month",
x = "Month", y = "Food Waste in kg")
boxplot_month_loss
# monthly boxplot on food waste ------------------------------------
boxplot_month_waste <-
ggplot(data = subset(df, is_closed %in% FALSE),
aes(x=month_name, y=food_waste_kg)) +
geom_boxplot(outlier.shape=8, outlier.size=2) +
stat_summary(fun=mean, geom="point", shape=16, size=2) +
labs(title = "Boxplot of Daily Food Waste in Month",
x = "Month", y = "Food Waste in kg")
boxplot_month_waste
# monthly boxplot on solid food waste ------------------------------------
boxplot_month_solidWaste <-
ggplot(data = subset(df, is_closed %in% FALSE),
aes(x=month_name, y=solid_waste_kg)) +
geom_boxplot(outlier.shape=8, outlier.size=2) +
stat_summary(fun=mean, geom="point", shape=16, size=2) +
labs(title = "Boxplot of Daily Solid Food Waste in Month",
x = "Monthy", y = "Solid Food Waste in kg")
boxplot_month_solidWaste
# boxplot of week of day for solid food waste ------------------------------------
boxplot_month_liquidWaste <-
ggplot(data = subset(df, is_closed %in% FALSE),
aes(x=month_name, y=liquid_waste_kg)) +
geom_boxplot(outlier.shape=8, outlier.size=2) +
stat_summary(fun=mean, geom="point", shape=16, size=2) +
labs(title = "Boxplot of Liquid Food Waste in Month",
x = "Month", y = "Liquid Food Waste in kg")
boxplot_month_liquidWaste
grid.arrange(boxplot_month_loss_waste, boxplot_month_loss,boxplot_month_waste,
boxplot_month_solidWaste,boxplot_month_liquidWaste)
Time Series Plots for Independents
## Time Series plots of:
# 1. weather conditions: temperature, humidity, precipitation
# 2. # orders + dine in + size + liquor + daily sales (confident)
# Time Series Plot on temperature ---------------------------------
tsPlot_temp <-
ggplot(data = df, aes(x = as.Date(date), y = temp_c)) +
geom_point() +
stat_smooth(method = "loess", color = "green", fill = "green") +
# geom_line(aes(group = 1), color="orange") +
geom_hline(aes(yintercept = 22), linetype='dotted') +
scale_x_date(date_labels = "%b %d") +
xlab("Date") + ylab("Temperature in Degree Celsius") +
ggtitle("Daily Average Hourly Temperature Plot")
tsPlot_temp## `geom_smooth()` using formula = 'y ~ x'
# Time Series Plot on gap temperature with 22C---------------------------------
tsPlot_temp_gap <-
ggplot(data = df, aes(x = as.Date(date), y = temp_c-22)) +
geom_point() +
stat_smooth(method = "loess", color = "green", fill = "green") +
# geom_line(color="green") +
scale_x_date(date_labels = "%b %d") +
xlab("Date") + ylab("Gap Temperature in Degree Celsius") +
ggtitle("Daily Gap Temperature Plot")
tsPlot_temp_gap## `geom_smooth()` using formula = 'y ~ x'
# Time Series Plot on humidity ---------------------------------
tsPlot_humidity <-
ggplot(data = df, aes(x = as.Date(date), y = humi_p)) +
geom_point() +
stat_smooth(method = "loess", color = "green", fill = "green") +
# geom_line(color="red") +
scale_x_date(date_labels = "%b %d") +
xlab("Date") + ylab("Humidity in Percent") +
ggtitle("Daily Humidity Plot")
tsPlot_humidity## `geom_smooth()` using formula = 'y ~ x'
# Time Series Plot on precipitation ---------------------------------
tsPlot_precip <-
ggplot(data = df, aes(x = as.Date(date), y = prcp_mm)) +
geom_point() +
stat_smooth(method = "loess", color = "green", fill = "green") +
# geom_line(color="blue") +
scale_x_date(date_labels = "%b %d") +
xlab("Date") + ylab("Precipitation in millimetre") +
ggtitle("Daily Precipitation Plot")
tsPlot_precip## `geom_smooth()` using formula = 'y ~ x'
grid.arrange(tsPlot_temp,tsPlot_temp_gap,tsPlot_humidity, tsPlot_precip)## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## Time Series plots of:
# 1. # orders (full, half, takeouts)
# 2. daily dine in served (kg)
# 4. liquor
# 5. daily sales (confident)
# Time Series Plot on Meal Orders ---------------------------------
tsPlot_total_orders <-
ggplot(data = subset(df, is_closed %in% FALSE), aes(x=as.Date(date))) +
geom_line(aes(y = fulls, color="fulls")) +
geom_line(aes(y = halfs, color="halfs")) +
scale_x_date(date_labels = "%b %d") +
geom_line(aes(y = takeouts, color="takeouts"), linetype = "dashed") +
xlab("Date") + ylab("Daily Number of Meal Orders") +
ggtitle("Daily Different Package Meal Orders Plot")+
scale_color_manual(name='Packages',
breaks=c('fulls', 'halfs', 'takeouts'),
values=c('fulls' = 'dark blue',
'halfs' = 'purple',
'takeouts'='dark red')) +
theme(legend.position = "right")
tsPlot_total_orders
# Time Series Plot on demand and production ---------------------------------
tsPlot_D_S <-
ggplot(data = subset(df, is_closed %in% FALSE), aes(x = as.Date(date))) +
geom_line(aes(y = daily_total_served), color="dark blue") +
geom_line(aes(y = FL_FP_kg), color="dark red", linetype = "dashed") +
scale_x_date(date_labels = "%b %d") +
xlab("Date") + ylab("Daily Quantity in kg") +
ggtitle("Daily Total Served and Production Plot") +
scale_color_manual(name='Served',
breaks=c('daily_total_served', 'FL_FP_kg'),
values=c('daily_total_served' = 'dark blue',
'FL_FP_kg' = 'dark red')) +
theme(legend.position = "right")
tsPlot_D_S
tsPlot_diff_D_S <-
ggplot(data = subset(df, is_closed %in% FALSE), aes(x = as.Date(date))) +
geom_line(aes(y = daily_total_served - FL_FP_kg), color="black") +
stat_smooth(aes(y = daily_total_served - FL_FP_kg), method = "loess",
color = "light green", fill = "light green") +
scale_x_date(date_labels = "%b %d") +
xlab("Date") + ylab("Daily Inventory in kg") +
ggtitle("Difference Between Total Served and Production Plot")
tsPlot_diff_D_S## `geom_smooth()` using formula = 'y ~ x'
# Time Series Plot on precipitation ---------------------------------
tsPlot_sales <-
ggplot(data = subset(df, is_closed %in% FALSE),aes(x = as.Date(date))) +
geom_line(aes(y = sales), color="purple") +
scale_x_date(date_labels = "%b %d") +
stat_smooth(aes(y = sales), method = "loess",
color = "light green", fill = "light green") +
xlab("Date") + ylab("Daily Sales in dollar") +
ggtitle("Daily Sales Plot")
tsPlot_sales## `geom_smooth()` using formula = 'y ~ x'
grid.arrange(tsPlot_total_orders,tsPlot_D_S, tsPlot_diff_D_S,tsPlot_sales)## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
(Partial and) Autocorrelation Function
## acf and pacf ----------------------------------------------------------------
# acf and pacf for food loss ---------------------------------------------------
acf_fl <- ggAcf(as.ts(df$food_loss_kg))
pacf_fl <- ggPacf(as.ts(df$food_loss_kg))
# acf and pacf for all food waste ----------------------------------------------
acf_fw <- ggAcf(as.ts(df$food_waste_kg))
pacf_fw <- ggPacf(as.ts(df$food_waste_kg))
# acf and pacf for solid food waste --------------------------------------------
acf_sfw <- ggAcf(as.ts(df$solid_waste_kg))
pacf_sfw <- ggPacf(as.ts(df$solid_waste_kg))
# acf and pacf for liquid food waste -------------------------------------------
acf_lfw <- ggAcf(as.ts(df$liquid_waste_kg))
pacf_lfw <- ggPacf(as.ts(df$liquid_waste_kg))
grid.arrange(acf_fl,pacf_fl)
grid.arrange(acf_fw,pacf_fw)
grid.arrange(acf_sfw,pacf_sfw)
grid.arrange(acf_lfw,pacf_lfw)
Spectral Analysis
# spectrum analysis for food loss ---------------------------------------------
# plot.spectrum(dt$allWasteKg)
raw.spec_fl <- list(spec.pgram(df$food_loss_kg, spans = 2))
1/raw.spec_fl[[1]]$freq[which.max(raw.spec_fl[[1]]$spec)]## [1] 3# spectrum analysis for food waste ---------------------------------------------
# plot.spectrum(dt$allWasteKg)
raw.spec_fw<- list(spec.pgram(df$food_waste_kg, spans = 2))
1/raw.spec_fw[[1]]$freq[which.max(raw.spec_fw[[1]]$spec)]## [1] 6# spectrum analysis for food waste ---------------------------------------------
# plot.spectrum(dt$allWasteKg)
raw.spec_sfw<- list(spec.pgram(df$solid_waste_kg, spans = 2))
1/raw.spec_sfw[[1]]$freq[which.max(raw.spec_sfw[[1]]$spec)]## [1] 8.571429# spectrum analysis for food waste ---------------------------------------------
# plot.spectrum(dt$allWasteKg)
raw.spec_lfw<- list(spec.pgram(df$liquid_waste_kg, spans = 2))
1/raw.spec_lfw[[1]]$freq[which.max(raw.spec_lfw[[1]]$spec)]## [1] 6roughly 6 (days) period for food waste, but food loss is approx. 3 days or 20 days cycle.
Q-Q plot
# Food loss ------------
ggplot(df, aes(sample=food_loss_kg)) +
stat_qq() + stat_qq_line() +
xlab("theoretical") + ylab("sample") +
ggtitle("QQ plot of Food Loss in kg")
# Food waste ------------
ggplot(df, aes(sample=food_waste_kg)) +
stat_qq() + stat_qq_line() +
xlab("theoretical") + ylab("sample") +
ggtitle("QQ plot of Food Waste in kg")
# Solid Food waste ------------
ggplot(df, aes(sample=solid_waste_kg)) +
stat_qq() + stat_qq_line() +
xlab("theoretical") + ylab("sample") +
ggtitle("QQ plot of Solid Food Waste in kg")
# Liquid Food waste ------------
ggplot(df, aes(sample=liquid_waste_kg)) +
stat_qq() + stat_qq_line() +
xlab("theoretical") + ylab("sample") +
ggtitle("QQ plot of Liquid Food Waste in kg")
Analysis
rm(list = ls()[! ls() %in% c("df", "AdjMat")])